Christian Ritz

Utilizing R Software Package for Dose-Response Studies: The Concept and Data Analysis

Advances in statistical software allow statistical methods for nonlinear regression analysis of dose-response curves to be carried out conveniently by non-statisticians. One such statistical software is the program R with the drc extension package. The drc package can: (1) simultaneously fit multiple dose-response curves; (2) compare curve parameters for significant differences; (3) calculate any point along the curve at the response level of interest, commonly known as an effective dose (e.g., ED30, ED50, ED90), and determine its significance; and (4) generate graphs for publications or presentations. We believe that the drc package has advantages that include: the ability to relatively simply and quickly compare multiple curves and select ED-levels easily along the curve with relevant statistics; the package is free of charge and does not require licensing fees, and the size of the package is only 70 MB. Therefore, our objectives are to: (1) provide a review of a few common issues in dose-response-curve fitting, and (2) facilitate the use of up-to-date statistical techniques for analysis of dose-response curves with this software. The methods described can be utilized to evaluate chemical and non-chemical weed control options. Benefits to the practitioners and academics are also presented.

Dose-Response Analysis Using R

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

Improved empirical models describing hormesis

During the past two decades, the phenomenon of hormesis has gained increased recognition. To promote research in hormesis, a sound statistical quantification of important parameters, such as the level and significance of the increase in response and the range of concentration where it occurs, is strongly needed. Here, we present an improved statistical model to describe hormetic dose-response curves and test for the presence of hormesis. Using the delta method and freely available software, any percentage effect dose or concentration can be derived with its associated standard errors. Likewise, the maximal response can be extracted and the growth stimulation calculated. The new model was tested on macrophyte data from multiple-species experiments and on laboratory data of Lemna minor. For the 51 curves tested, significant hormesis was detected in 18 curves, and for another 17 curves, the hormesis model described that data better than the logistic model did. The increase in response ranged from 5 to 109%. The growth stimulation occurred at an average dose somewhere between zero and concentrations corresponding to approximately 20 to 25% of the median effective concentration (EC50). Testing the same data with the hormesis model proposed by Brain and Cousens in 1989, we found no significant hormesis. Consequently, the new model is shown to be far more robust than previous models, both in terms of variation in data and in terms of describing hormetic effects ranging from small effects of a 10% increase in response up to effects of an almost 100% increase in response.

Toward a unified approach to dose-response modeling in ecotoxicology.

This study reviews dose-response models that are used in ecotoxicology. The focus lies on clarification of differences and similarities between models, and as a side effect, their different guises in ecotoxicology are unravelled. A look at frequently used dose-response models reveals major discrepancies, among other things in naming conventions. Therefore, there is a need for a unified view on dose-response modeling in order to improve the understanding of it and to facilitate communication and comparison of findings across studies, thus realizing its full potential. This study attempts to establish a general framework that encompasses most dose-response models that are of interest to ecotoxicologists in practice. The framework includes commonly used models such as the log-logistic and Weibull models, but also features entire suites of models as found in various guidance documents. An outline on how the proposed framework can be implemented in statistical software systems is also provided.

Contribution of gastroenteropancreatic appetite hormones to protein-induced satiety

BACKGROUND Effects of protein intake on appetite-regulating hormones and their dynamics are unclear. OBJECTIVES We investigated the satiating effects of meals with varying protein contents and whether there was an effect of dose on appetite-regulating hormones and appetite ratings. DESIGN Twenty-five men [mean ± SD age: 30.0 ± 8.7 y; body mass index (BMI; in kg/m(2)): 25.9 ± 4.7] participated in the 3-way, randomized, double-blind crossover study. Test meals were isocaloric with 30% of energy from fat and protein content adjusted at the expense of carbohydrate. Test meals were normal protein (NP; 14% of energy from protein), medium-high protein (MHP; 25% of energy from protein), and high protein (HP, 50% of energy from protein). Appetite ratings and blood samples were assessed every 0.5 h for 4 h. An ad libitum lunch was served 4 h after the meal. RESULTS Protein increased dose-dependently glucagon-like peptide-1 (GLP-1), peptide YY (PYY) 3-36, and glucagon; MHP produced 10%, 7%, and 47% greater responses, respectively; and HP produced 20%, 14%, and 116% greater responses, respectively, than did NP (P < 0.03). Compared with NP, HP increased insulin and cholecystokinin and decreased ghrelin and glucose-dependent insulinotropic polypeptide (P < 0.05). Satiety and fullness dose-dependently increased by 7% and 6% for MHP and 16% and 19% for HP compared with NP (P < 0.001). Hunger and prospective consumption dose-dependently decreased by 15% and 13% for MHP and by 25% and 26% for HP compared with NP (P < 0.0003). There was a combined effect of GLP-1 and PYY 3-36 (P = 0.03) next to the additive effect of GLP-1 (P = 0.006) on the composite appetite score. No difference was shown in ad libitum energy intake. CONCLUSION Protein dose-dependently increased satiety and GLP-1, PYY 3-36, and glucagon, which may, at least in part, be responsible for the satiety-stimulating effect of protein. This trial was registered at clinicaltrials.gov as NCT01561235.

Review: Confirmation of Resistance to Herbicides and Evaluation of Resistance Levels

Abstract As cases of resistance to herbicides escalate worldwide, there is increasing demand from growers to test for weed resistance and learn how to manage it. Scientists have developed resistance-testing protocols for numerous herbicides and weed species. Growers need immediate answers and scientists are faced with the daunting task of testing an increasingly large number of samples across a variety of species and herbicides. Quick tests have been, and continue to be, developed to address this need, although classical tests are still the norm. Newer methods involve molecular techniques. Whereas the classical whole-plant assay tests for resistance regardless of the mechanism, many quick tests are limited by specificity to an herbicide, mode of action, or mechanism of resistance. Advancing knowledge in weed biology and genomics allows for refinements in sampling and testing protocols. Thus, approaches in resistance testing continue to diversify, which can confound the less experienced. We aim to help weed science practitioners resolve questions pertaining to the testing of herbicide resistance, starting with field surveys and sampling methods, herbicide screening methods, data analysis, and, finally, interpretation. More specifically, this article discusses approaches for sampling plants for resistance confirmation assays, provides brief overviews on the biological and statistical basis for designing and analyzing dose–response tests, and discusses alternative procedures for rapid resistance confirmation, including molecular-based assays. Resistance confirmation procedures often need to be slightly modified to suit a specific situation; thus, the general requirements as well as pros and cons of quick assays and DNA-based assays are contrasted. Ultimately, weed resistance testing research, as well as resistance management decisions arising from research, needs to be practical, feasible, and grounded in science-based methods.

Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry

BackgroundFitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time PCR (qPCR) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal qPCR data.ResultsThe four-parameter models were compared to their five-parameter counterparts by means of nested F-tests based on the residual variance, thus acquiring a statistical measure for higher performance. For nearly all qPCR data we examined, five-parameter models resulted in a significantly better fit. Furthermore, accuracy and precision for the estimation of efficiencies and calculation of quantitative ratios were assessed with four independent dilution datasets and compared to the most commonly used quantification methods. It could be shown that the five-parameter model exhibits an accuracy and precision more similar to the non-sigmoidal quantification methods.ConclusionThe five-parameter sigmoidal models outperform the established four-parameter model with high statistical significance. The estimation of essential PCR parameters such as PCR efficiency, threshold cycles and initial template fluorescence is more robust and has smaller variance. The model is implemented in the qpcR package for the freely available statistical R environment. The package can be downloaded from the authors homepage.

Fatness predicts decreased physical activity and increased sedentary time, but not vice versa: support from a longitudinal study in 8- to 11-year-old children

Objective:To examine independent and combined cross-sectional associations between movement behaviors (physical activity (PA), sedentary time, sleep duration, screen time and sleep disturbance) and fat mass index (FMI), as well as to examine longitudinal associations between movement behaviors and FMI.Methods:Cross-sectional and longitudinal analyses were done using data from the OPUS school meal study on 785 children (52% boys, 13.4% overweight, ages 8–11 years). Total PA, moderate-to-vigorous PA (MVPA), sedentary time and sleep duration (7 days and 8 nights) were assessed by an accelerometer and FMI was determined by dual-energy X-ray absorptiometry (DXA) on three occasions over 200 days. Demographic characteristics, screen time and sleep disturbance (Children’s Sleep Habits Questionnaire) were also obtained.Results:Total PA, MVPA and sleep duration were negatively associated with FMI, while sedentary time and sleep disturbances were positively associated with FMI (P⩽0.01). However, only total PA, MVPA and sleep duration were independently associated with FMI after adjustment for multiple covariates (P<0.001). Nevertheless, combined associations revealed synergistic effects among the different movement behaviors. Changes over time in MVPA were negatively associated with changes in FMI (P<0.001). However, none of the movement behaviors at baseline predicted changes in FMI (P>0.05), but higher FMI at baseline predicted a decrease in total PA and MVPA, and an increase in sedentary time (P⩽0.001), even in normal-weight children (P⩽0.03).Conclusion:Total PA, MVPA and sleep duration were independently associated with FMI, and combined associations of movement behaviors showed a synergistic effect with FMI. In the longitudinal study design, a high FMI at baseline was associated with lower PA and higher sedentary time after 200 days but not vice versa, even in normal-weight children. Our results suggest that adiposity is a better predictor of PA and sedentary behavior changes than the other way around.

Consumption of a diet low in advanced glycation end products for 4 weeks improves insulin sensitivity in overweight women

OBJECTIVE High-heat cooking of food induces the formation of advanced glycation end products (AGEs), which are thought to impair glucose metabolism in type 2 diabetic patients. High intake of fructose might additionally affect endogenous formation of AGEs. This parallel intervention study investigated whether the addition of fructose or cooking methods influencing the AGE content of food affect insulin sensitivity in overweight individuals. RESEARCH DESIGN AND METHODS Seventy-four overweight women were randomized to follow either a high- or low-AGE diet for 4 weeks, together with consumption of either fructose or glucose drinks. Glucose and insulin concentrations—after fasting and 2 h after an oral glucose tolerance test—were measured before and after the intervention. Homeostasis model assessment of insulin resistance (HOMA-IR) and insulin sensitivity index were calculated. Dietary and urinary AGE concentrations were measured (liquid chromatography tandem mass spectrometry) to estimate AGE intake and excretion. RESULTS When adjusted for changes in anthropometric measures during the intervention, the low-AGE diet decreased urinary AGEs, fasting insulin concentrations, and HOMA-IR, compared with the high-AGE diet. Addition of fructose did not affect any outcomes. CONCLUSIONS Diets with high AGE content may increase the development of insulin resistance. AGEs can be reduced by modulation of cooking methods but is unaffected by moderate fructose intake.

Low physical activity level and short sleep duration are associated with an increased cardio-metabolic risk profile: a longitudinal study in 8-11 year old Danish children.

Background As cardio-metabolic risk tracks from childhood to adulthood, a better understanding of the relationship between movement behaviors (physical activity, sedentary behavior and sleep) and cardio-metabolic risk in childhood may aid in preventing metabolic syndrome (MetS) in adulthood. Objective To examine independent and combined cross-sectional and longitudinal associations between movement behaviors and the MetS score in 8-11 year old Danish children. Design Physical activity, sedentary time and sleep duration (seven days and eight nights) were assessed by accelerometer and fat mass index (fat mass/height2) was assessed using Dual-energy X-ray absorptiometry. The MetS-score was based on z-scores of waist circumference, mean arterial blood pressure, homeostatic model assessment of insulin resistance, triglycerides and high density lipoprotein cholesterol. All measurements were taken at three time points separated by 100 days. Average of the three measurements was used as habitual behavior in the cross-sectional analysis and changes from first to third measurement was used in the longitudinal analysis. Results 723 children were included. In the cross-sectional analysis, physical activity was negatively associated with the MetS-score (P<0.03). In the longitudinal analysis, low physical activity and high sedentary time were associated with an increased MetS-score (all P<0.005); however, after mutual adjustments for movement behaviors, physical activity and sleep duration, but not sedentary time, were associated with the MetS-score (all P<0.03). Further adjusting for fat mass index while removing waist circumference from the MetS-score rendered the associations no longer statistically significant (all P>0.17). Children in the most favorable tertiles of changes in moderate-to-vigorous physical activity, sleep duration and sedentary time during the 200-day follow-up period had an improved MetS-score relative to children in the opposite tertiles (P = 0.005). Conclusion The present findings indicate that physical activity, sedentary time and sleep duration should all be targeted to improve cardio-metabolic risk markers in childhood; this is possibly mediated by adiposity.